端到端的梯度提升网络分类过程可视化

Classification Process Visualization of End-to-end Gradient Boosting Network

端到端的梯度提升网络是由多个基学习器集合而成的神经网络。它与残差网络结构上有相似之处,二者后面的网络单元(学习器或残差块)都在前面网络单元的基础上继续学习,以逐渐逼近目标函数。端到端的梯度提升网络其网络结构较为复杂,我们对其工作机制的理解还不足。可视化技术有助于我们直观地理解网络内部的工作机制。本文着眼于探究端到端的梯度提升网络的分类过程和特点,在模拟数据上对其分类过程进行了可视化,通过与全连接网络和残差网络的对比突出其特点和问题,并利用哑节点说明其自正则能力相对较弱。然后,利用可视化方法探索了学习率对其分类过程的影响。最后,通过实际分类任务上的实验,在一定程度上验证了可视化相关结论的正确性。

End-to-end gradient boosting network is a neural network composed of multiple base learners. It is similar to the structure of residual network. The behind network units(learners or residual blocks)in the two networks continue to learn on the basis of the former network units to gradually approach the objective function. The network structure of end-to-end gradient boosting network is more complex,and we have not enough understanding of its working mechanism. Visualization technology is helpful for us to intuitively understand the working mechanism within the network. This paper focuses on exploring the classification process and characteristics of end-to-end gradient boosting network. We visualized the classification process of it on simulated data,highlights its characteristics and problems by comparing with fully connected network and residual network. We use dummy nodes to show that its self regularization ability is relatively weak. Then,the visual method is used to explore the influence of learning rate on the classification process. Finally,through the experiment on the actual classification task,the correctness of the visualization related conclusions is verified to a certain extent.